MatchBackgroundsTask

class lsst.pipe.tasks.matchBackgrounds.MatchBackgroundsTask(*args, **kwargs)

Bases: lsst.pipe.base.Task

Methods Summary

emptyMetadata()	Empty (clear) the metadata for this Task and all sub-Tasks.
getAllSchemaCatalogs()	Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.
getFullMetadata()	Get metadata for all tasks.
getFullName()	Get the task name as a hierarchical name including parent task names.
getName()	Get the name of the task.
getSchemaCatalogs()	Get the schemas generated by this task.
getTaskDict()	Get a dictionary of all tasks as a shallow copy.
makeField(doc)	Make a lsst.pex.config.ConfigurableField for this task.
makeSubtask(name, **keyArgs)	Create a subtask as a new instance as the name attribute of this task.
matchBackgrounds(refExposure, sciExposure)	Match science exposure’s background level to that of reference exposure.
run(expRefList, expDatasetType[, …])	Match the backgrounds of a list of coadd temp exposures to a reference coadd temp exposure.
selectRefExposure(expRefList, …)	Find best exposure to use as the reference exposure.
timer(name, logLevel)	Context manager to log performance data for an arbitrary block of code.

Methods Documentation

emptyMetadata() → None

Empty (clear) the metadata for this Task and all sub-Tasks.

getAllSchemaCatalogs() → Dict[str, Any]

Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.

Returns:

schemacatalogs : dict

Keys are butler dataset type, values are a empty catalog (an instance of the appropriate lsst.afw.table Catalog type) for all tasks in the hierarchy, from the top-level task down through all subtasks.

Notes

This method may be called on any task in the hierarchy; it will return the same answer, regardless.

The default implementation should always suffice. If your subtask uses schemas the override Task.getSchemaCatalogs, not this method.

getFullMetadata() → lsst.pipe.base._task_metadata.TaskMetadata

Get metadata for all tasks.

Returns:

metadata : TaskMetadata

The keys are the full task name. Values are metadata for the top-level task and all subtasks, sub-subtasks, etc.

Notes

The returned metadata includes timing information (if @timer.timeMethod is used) and any metadata set by the task. The name of each item consists of the full task name with . replaced by :, followed by . and the name of the item, e.g.:

topLevelTaskName:subtaskName:subsubtaskName.itemName

using : in the full task name disambiguates the rare situation that a task has a subtask and a metadata item with the same name.

getFullName() → str

Get the task name as a hierarchical name including parent task names.

Returns:

fullName : str

The full name consists of the name of the parent task and each subtask separated by periods. For example:

• The full name of top-level task “top” is simply “top”.
• The full name of subtask “sub” of top-level task “top” is “top.sub”.
• The full name of subtask “sub2” of subtask “sub” of top-level task “top” is “top.sub.sub2”.

getName() → str

Get the name of the task.

Returns:

taskName : str

Name of the task.

See also

getFullName

getSchemaCatalogs() → Dict[str, Any]

Get the schemas generated by this task.

Returns:

schemaCatalogs : dict

Keys are butler dataset type, values are an empty catalog (an instance of the appropriate lsst.afw.table Catalog type) for this task.

See also

Task.getAllSchemaCatalogs

Notes

Warning

Subclasses that use schemas must override this method. The default implementation returns an empty dict.

This method may be called at any time after the Task is constructed, which means that all task schemas should be computed at construction time, not when data is actually processed. This reflects the philosophy that the schema should not depend on the data.

Returning catalogs rather than just schemas allows us to save e.g. slots for SourceCatalog as well.

getTaskDict() → Dict[str, weakref.ReferenceType[lsst.pipe.base.task.Task]]

Get a dictionary of all tasks as a shallow copy.

Returns:

taskDict : dict

Dictionary containing full task name: task object for the top-level task and all subtasks, sub-subtasks, etc.

classmethod makeField(doc: str) → lsst.pex.config.configurableField.ConfigurableField

Make a lsst.pex.config.ConfigurableField for this task.

Parameters:

doc : str

Help text for the field.

Returns:

configurableField : lsst.pex.config.ConfigurableField

A ConfigurableField for this task.

Examples

Provides a convenient way to specify this task is a subtask of another task.

Here is an example of use:

class OtherTaskConfig(lsst.pex.config.Config):
    aSubtask = ATaskClass.makeField("brief description of task")

makeSubtask(name: str, **keyArgs) → None

Create a subtask as a new instance as the name attribute of this task.

Parameters:

name : str

Brief name of the subtask.

keyArgs

Extra keyword arguments used to construct the task. The following arguments are automatically provided and cannot be overridden:

• “config”.
• “parentTask”.

Notes

The subtask must be defined by Task.config.name, an instance of ConfigurableField or RegistryField.

matchBackgrounds(refExposure, sciExposure)

Match science exposure’s background level to that of reference exposure.

Process creates a difference image of the reference exposure minus the science exposure, and then generates an afw.math.Background object. It assumes (but does not require/check) that the mask plane already has detections set. If detections have not been set/masked, sources will bias the background estimation.

The ‘background’ of the difference image is smoothed by spline interpolation (by the Background class) or by polynomial interpolation by the Approximate class. This model of difference image is added to the science exposure in memory.

Fit diagnostics are also calculated and returned.

Parameters:

refExposure : lsst.afw.image.Exposure

Reference exposure.

sciExposure : lsst.afw.image.Exposure

Science exposure; modified by changing the background level to match that of the reference exposure.

Returns:

model : lsst.pipe.base.Struct

Background model as a struct with attributes:

backgroundModel

An afw.math.Approximate or an afw.math.Background.

fitRMS

RMS of the fit. This is the sqrt(mean(residuals**2)), (float).

matchedMSE

The MSE of the reference and matched images: mean((refImage - matchedSciImage)**2); should be comparable to difference image’s mean variance (float).

diffImVar

The mean variance of the difference image (float).

run(expRefList, expDatasetType, imageScalerList=None, refExpDataRef=None, refImageScaler=None)

Match the backgrounds of a list of coadd temp exposures to a reference coadd temp exposure.

Choose a refExpDataRef automatically if none supplied.

Parameters:

expRefList : list

List of data references to science exposures to be background-matched; all exposures must exist.

expDatasetType : str

Dataset type of exposures, e.g. ‘goodSeeingCoadd_tempExp’.

imageScalerList : list, optional

List of image scalers (coaddUtils.ImageScaler); if None then the images are not scaled.

refExpDataRef : Unknown, optional

Data reference for the reference exposure. If None, then this task selects the best exposures from expRefList. If not None then must be one of the exposures in expRefList.

refImageScaler : Unknown, optional

Image scaler for reference image; ignored if refExpDataRef is None, else scaling is not performed if None.

Returns:

result : lsst.pipe.base.Struct

Results as a struct with attributes:

backgroundInfoList

A list of pipeBase.Struct, one per exposure in expRefList, each of which contains these fields: - isReference: This is the reference exposure (only one

returned Struct will contain True for this value, unless the ref exposure is listed multiple times).

• backgroundModel: Differential background model

  (afw.Math.Background or afw.Math.Approximate). Add this to the science exposure to match the reference exposure.

• fitRMS: The RMS of the fit. This is the sqrt(mean(residuals**2)).

• matchedMSE: The MSE of the reference and matched images:

  mean((refImage - matchedSciImage)**2);

  should be comparable to difference image’s mean variance.

• diffImVar: The mean variance of the difference image.

All fields except isReference will be None if isReference True or the fit failed.

Raises:

RuntimeError

Raised if an exposure does not exist on disk.

selectRefExposure(expRefList, imageScalerList, expDatasetType)

Find best exposure to use as the reference exposure.

Calculate an appropriate reference exposure by minimizing a cost function that penalizes high variance, high background level, and low coverage. Use the following config parameters: - bestRefWeightCoverage - bestRefWeightVariance - bestRefWeightLevel

Parameters:

expRefList : list

List of data references to exposures. Retrieves dataset type specified by expDatasetType. If an exposure is not found, it is skipped with a warning.

imageScalerList : list

List of image scalers (coaddUtils.ImageScaler); must be the same length as expRefList.

expDatasetType : str

Dataset type of exposure: e.g. ‘goodSeeingCoadd_tempExp’.

Returns:

bestIdx : int

Index of best exposure.

Raises:

RuntimeError

Raised if none of the exposures in expRefList are found.

timer(name: str, logLevel: int = 10) → Iterator[None]

Context manager to log performance data for an arbitrary block of code.

Parameters:

name : str

Name of code being timed; data will be logged using item name: Start and End.

logLevel

A logging level constant.

See also

timer.logInfo

Examples

Creating a timer context:

with self.timer("someCodeToTime"):
    pass  # code to time